


'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class        _UpperCamelCase	(    lowercase__):


				'''simple docstring'''


				_snake_case						       =				['''image_processor''', '''tokenizer''']
				_snake_case						       =				'''CLIPImageProcessor'''
				_snake_case						       =				('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')

				def __init__(     self							,      a_=None							,      a_=None							,      **a_							)    ->  int:
					lowercase						:		Union[str, Any]   					=		None
					if "feature_extractor" in kwargs:
						warnings.warn(
						    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
						    " instead."							,      SCREAMING_SNAKE_CASE_							,      )
						lowercase						:		Optional[Any]   					=		kwargs.pop("feature_extractor"							)

					lowercase						:		int   					=		image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError("You need to specify an `image_processor`."							)
					if tokenizer is None:
						raise ValueError("You need to specify a `tokenizer`."							)

					super().__init__(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)

				def __call__(     self							,      a_=None							,      a_=None							,      a_=None							,      **a_							)    ->  List[Any]:


					if text is None and images is None:
						raise ValueError("You have to specify either text or images. Both cannot be none."							)

					if text is not None:
						lowercase						:		Dict   					=		self.tokenizer(SCREAMING_SNAKE_CASE_							,      return_tensors=SCREAMING_SNAKE_CASE_							,      **SCREAMING_SNAKE_CASE_							)

					if images is not None:
						lowercase						:		List[Any]   					=		self.image_processor(SCREAMING_SNAKE_CASE_							,      return_tensors=SCREAMING_SNAKE_CASE_							,      **SCREAMING_SNAKE_CASE_							)

					if text is not None and images is not None:
						lowercase						:		List[str]   					=		image_features.pixel_values
						return encoding
					elif text is not None:
						return encoding
					else:
						return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_							)							,      tensor_type=SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							,      *a_							,      **a_							)    ->  Optional[Any]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_							,      **SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							,      *a_							,      **a_							)    ->  Any:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_							,      **SCREAMING_SNAKE_CASE_							)


				@property
				def 	a__		(     self							)    ->  Dict:
					lowercase						:		int   					=		self.tokenizer.model_input_names
					lowercase						:		int   					=		self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use PerceiverImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


